How can you extract insights from unstructured data?
The digital age has brought with it an enormous volume of data, 80% of which is unstructured. Traditionally, this data would have been rendered useless and discarded.
However, today, businesses understand the value of Unstructured Data yet most business managers still find it hard to process unstructured data
In fact,95% of businesses don’t have the required expertise to process big data.
But the business world is unforgiving and only the strongest survive. In the 21st Century, the strongest businesses are those that can collect data and harness information both from internal operations and from customers.
Your move in search for information on how to handle unstructured data and extract insights to enhance your business decision making is a step in the right direction.
Contrary to what many professionals think, analyzing unstructured data today with AI is quite simple.
So, here’s a 5 step procedure to follow in order to extract structured data insights from unstructured data
Step 1: Data Collection
Analyzing unstructured data begins with having the data itself, and the guiding principle for data collection is having the end goal in mind.
What do you want to accomplish with this data?
Are you looking to find statistics or follow trends in the market?
Do you want to find out what your customers are saying about your brand?
Start with a solid understanding of what you want to accomplish. If the end result is not clear, you will not collect quality data, and your analysis will be unusable.
Then, determine the methods of data collection to use.
For instance, documents contain valuable insights on internal business operations. By using Intelligent Document Processing, you can digitize data from documents and make it searchable.
Meanwhile, survey responses are good for opinions on your products, while social media posts work well with brand perception information.
The type of data you collect will also determine the method of analysis to use for conversion of unstructured data to structured data.
Whichever method you use to collect data, make sure no data is lost. Do this by storing the information indata lakes; repositories that store data in raw format.
The features to consider when choosing data retrieval and storage depend on the size, scalability, velocity, and specific needs.
You can also consider integrating software you already use to help extract unstructured data and operate from a centralized dashboard.
Ultimately, make sure you collect the right data because this whole process is founded on it.
Step 2: Data Cleaning
While it’s necessary to keep all data in the original formats, you cannot analyze it, without taking out irrelevant text and symbols that give you no qualitative value.
Therefore, clearing clutter is a necessary step in processing unstructured data.
For instance, social media data can contain a lot of mistakes, missing data, misspellings, slang, bad grammar, all of which can affect the quality of your data.
Start by making a copy of original data and work on the copy.
Then use AI-powered tools to perform word processing tasks like running grammar and spell check, removing special characters such as signatures, images, links and repeated words.
Using a tool with text normalization feature can help reduce the amount of different information to be processed therefore improving analysis efficiency.
If you collect data from multiple sources, the need for data cleaning increases, since you have to unify the different representations of the same data.
For instance data extracted by IDP can have numerous formats which need standardization before analysis. Trying to discover how to handle unstructured data while in non-uniform formats will be tedious and could be a dead end.
Machine learning eases the process of transforming data into a stable format, and maintaining consistent patterns across all data collection points. Implementing AI in cleaning data therefore helps.
Data cleaning is a critical step in converting unstructured data to structured data since it determines how efficient the analysis will be conducted.
Step 3: Data Structuring
Machine learning software is instrumental in the breaking down of unstructured data to structured data and most tools will have guides on how to extract structured data insights from unstructured data
Normally, it involves an annotation tool which extracts entities from texts.
You begin by defining the entities and assigning roles, for instance, Chicago can have two roles “destination” and “geopolitical”
Entities can also be part of core referencing in the sentence ‘The house was flooded, but it suffered no structural damage’, house and it represents the same entity.
If you are defining entities and relationships in a highly technical field, having a subject matter expert can help improve accuracy and expedite the machine learning process.
Next you implement human annotation to help in training the machine. Humans highlight text and select entities to be tagged, in the process training the machine how to create relationships.
To automate the data structuring, you’ll need to create and deploy a pre-annotator. Since the human annotators provide the machine learning algorithm with a template to work with, designing a pre-annotator will be seamless.
A pre-annotator automatically structures the data based on a set template.
For instance, AI Contract Analytics involves annotation of data in processing of multiple contract formats. It can identify hundreds of common data points and clauses in contracts to streamline contract review.
Using AI-powered annotation software greatly expedites data structuring .
With time and right training, your model becomes powerful enough for processing and analyzing unstructured data with great accuracy.
Step 4: Data Analysis
Due to the defined patterns of the now structured data you can begin processing unstructured data through text mining.
Text mining is a process of examining large collections of data to discover relevant information when converting unstructured data to structured data.
A variety of analysis methodologies are available, butNatural Language Processing (NLP) is one of the most effective. Using NLP, you can organize data and perform many automated tasks necessary for data analysis.
Depending on your goals, you can analyze data to obtain the metrics you need using different extraction and classification methods.
For instance, AI text summarization software understands the context of data and produces the most important sentences in order. Thus, it can summarize an entire novel into a few pages of sentences with the most valuable information.
AI based sentiment analysis uses NLP to process large amounts of data from many sources to understand the emotion of the writer.
It can determine whether a piece of writing is positive, negative or neutral to show how people feel about a particular topic
For instance in customer service tickets, it’s used to determine the level of satisfaction or dissatisfaction of the customer and prioritize the most pressing issues or categorize tickets based on topic, and route them to relevant departments.
Basing your analysis on your end goal, you can provide a machine learning tool with guidelines to provide valuable insights.
Step 5: Drawing Insights
After carefully analyzing data you can begin drawing insights.
You can see how customers’ opinions change over time, brand perception from place to place and how a company’s operations affect the overall productivity and revenue of the company.
The best place to start, however, is by creating charts and graphs to visualize your data. Some AI based data intelligence platforms help you in visualizing such results to give you a better experience in insight formulation.
AI based insight formulation follows a certain criterion after uploading data on an insights tool;
- The tool begins to extract information in the same manner as industry experts
- A knowledge graph feature changes the information extracted into knowledge
- Artificial intelligence uses stored knowledge, to find key business insights
By replacing manual insights formation in the analyzing stage of how to handle unstructured data, you can reduce room for error significantly while saving cost on the human resource.
As you analyze data, remember your objective. Beyond a basic view of your analytics you should use data to realize insights that enhance your business operations.
In addition, when using machine learning models to extract insights, operate in a continuous improvement environment to improve the quality of data collection analysis and decision making.
Conclusion
Artificial intelligence has greatly enhanced data processing in every facet imaginable. In processing unstructured data, it appears in all steps, expediting tasks and achieving high accuracy.
Courtesy of this technology, as a business manager, you have little to worry about in your goal to make data driven decisions with the help of unstructured data. This digital era is characterized by numerous tools that can help you collect any form of data.
So the only challenge you had was selecting the right strategies and tools to use for analyzing unstructured data.
Now, with this guide on how to extract structured data insights from unstructured data, you can focus on finding the right tools.
